"Low-certainty modals not future tenses cause increased psychological discounting in English relative to Dutch"

📢 New paper from: Cole Robertson, Seán Roberts (seangroberts.bsky.social), Asifa Majid, Tammy Lu, Philip Wolff, & Robin Dunbar

www.sciencedirect.com/science/arti...

The implications are broad. From savings to health to climate action, the ways languages express the future can bias how people plan. Rethinking the FTR–behavior link through modality opens new doors. New in Cognition!

This adds a new twist to linguistic relativity: Grammar nudges our sense of how valuable future events really are, because of how certain we are about them. We call this the modal hypothesis (Fig 3)

It’s not future tenses like will that drive this effect. Instead, the obligation in English to use a modal like may lowers its perceived probability (i.e. Sub-Figure C was supported but not A or B in Fig 2). This makes the future feel less certain and therefore less valuable.

How did we study this? We combined experiments, bilingual comparisons, and large-scale text analyses. Together these show that English future grammar shifts both how people talk about and evaluate the future, note higher use of low-certainty modals in English (Fig 1).

Discounting is our tendency to devalue things that are far away in time or have a low probability of happening. The stronger the discounting, the harder it is to save money, stick to health goals, or act on climate change.

Critically, objective measures of spatial and temporal estimation did not correlate with memory quality or condition, challenging the assumption that vivid episodic recall depends on precise spatiotemporal encoding.

Read it here: www.sciencedirect.com/science/arti...

Findings: Immersive movement produced richer, event-specific free recall; joystick control yielded more generalized/semantic-like descriptions—knowledge accrued over repeated routes.

"Feature-based Filtering Determines Object-based Selection in the Attentional Boost Effect"

📢New paper from: Juyeon Joe, Yoongeol Yang, & Min-Shik Kim

www.sciencedirect.com/science/arti...

These findings reveal that the ABE depends on feature-based filtering that governs object-level selection, challenging purely temporal accounts of the effect and highlighting the interplay between attention, perception, and memory.

In our new paper, we tested if this boost arises from temporal selection or from object-based mechanisms. Across two RSVP exps, we found that only stimuli sharing all defining features of the target benefited from the boost, whereas partial feature overlap produced no advantage.

Conclusion: The way confidence is derived from motion representations depends on whether perception is discrete or continuous.
Perceptual confidence has near perfect access to the existence of discrete representations, but only weak access to precision.

A trial-wise analysis reveals a very strong correlation between confidence and guessing rate for transparent motion, but extremely weak correlation between confidence and precision (SD) for both motion types.

Exp. 3: And more so, this dissociation was also present when both discrete and continuous motion stimulus were presented intermixed (not a task set effect!).

Exp. 2: When motion stimuli induce continuous local motion pooling with large differences in response precision, confidence does track precision, but surprisingly the relationship between confidence and precision was extremely weak.

Exp. 1: When motion stimuli induce all-or-none segmentation of signal and noise dots, confidence has near perfect access to the existence of discrete representations, but no access to precision.
Perhaps precision was very high and invariant. We tested it!

How is perceptual confidence derived from internal representations?
A difficult question because the nature of internal representations is an ongoing debate.
We leverage recent findings into how global motion is represented to answer this question.

New article: “Emotion meets attention: The role of left-to-right valence mapping in an exogenous cueing task"

Authors: Federico D’Atri, Mauro Murgia, Valter Prpic, Carlo Fantoni

Read it here: authors.elsevier.com/sd/article/S...